Deng, Yue <br/><br/>
Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.  [electronic resource] 

 -  Nature methods  04 2019 
 - 311-314 p.  digital 
<br/><br/> Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't 
<br/><br/><label>ISSN: </label>1548-7105 
<br/><br/><label>Standard No.: </label>10.1038/s41592-019-0353-7  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Algorithms<br/>Animals<br/>Brain Mapping<br/>Cluster Analysis<br/>Computational Biology--methods<br/>Computer Simulation<br/>Databases, Genetic<br/>Deep Learning<br/>Gene Expression Profiling<br/>Inflammation<br/>Intestines--cytology<br/>Leukocytes, Mononuclear--cytology<br/>Mice<br/>Phenotype<br/>Principal Component Analysis<br/>RNA--analysis<br/>Reproducibility of Results<br/>Retina--metabolism<br/>Sequence Analysis, RNA<br/>Single-Cell Analysis<br/>Software<br/>Transcriptome